Despite the buzz around digital transformation, a staggering 74% of insurance companies still use legacy systems to carry out their daily operations.

Hindsight has been the guiding light of risk management. Underwriters have used backward-looking data to evaluate risks, and loss events have been the driver of policy adjustments.

However, this method is now rapidly losing ground, thanks to the revolution of AI and IoT.

The current climate of volatility, looming cyber threats and supply chain fragility have created a world of escalating risks requiring more than a basic reactive model. Insurer needs something smarter, a more forward-looking approach that deeply involves tech in risk strategy.

That's where the Internet of Things and artificial intelligence are driving change. Forming the heart of the future of insurance risk management, their combined powers transform static risk profiles into dynamic systems that are capable of predicting, detecting and even preventing losses in real time. Powered by machine-learning algorithms, these systems don't just flag risks -- they consistently learn and adapt so insurers can be one step ahead of the risk lifecycle.

In this article, we will explore how AI and IoT are redefining the insurance landscape with advanced technologies like real-time risk scoring and hyper-personalized coverage.

We'll also discuss practical uses, hurdles to implementation and what insurers like you need to stay ahead of the curve.

The Evolution of Risk Management in the Insurance Industry

Once upon a time, risk management was a manual process, heavily dependent on spreadsheets, static questionnaires, and actuarial tables. But there's been a dynamic shift since then, ushering in brand new processes of real-time data analysis and algorithmic decision-making.

On the surface, it might seem like an optional shift. It is anything but.

As regulatory bodies demand greater transparency and faster reporting, customers are seeking more personalized support and responsive coverage. This massive shift makes the proverbial one-size-fits-all policies obsolete.

Armed with data-driven risk models instead, insurers can now easily leverage the insights that structured and unstructured data provide to make swift and accurate underwriting decisions. This data can come from anywhere -- be it financials and claims history or telematics and weather feed.

With predictive analysis, you can now keep an eagle eye on trends before they escalate. You can also adjust policies on the basis of real-time exposure and behavior with dynamic underwriting.

Calling these innovations revolutionary will be no understatement. The convergence of AI and IoT has transformed risk from something to price and transfer to a process that involves anticipation, monitoring and management.

The era of the retrospective stance is over as the age of forward-leaning approach takes over with AI and IoT in the driver's seat.

Role of IoT in Real-Time Risk Detection and Prevention

The Internet of Things or IoT might be best-known for connecting devices. But its not-so-glamorous role of helping insurers detect, assess and mitigate risks in real time is just as critical.

Devices such as telematics in vehicles, wearables, smart home sensors and industrial IoT create a continuous loop of feedback between insurers and insured assets.

How do these devices and their always-on data stream change the game? Take smart thermostats. These can spot a frozen pipe before it bursts. Meanwhile, telematics can identify high-risk patterns from a person's driving behavior even before an accident takes place. Industrial sensors can prevent workplace accidents by flagging faulty machines. Each data point can prevent critical loss.

That's why insurers now rely on IoT for multiple tasks, including sharing more alerts and building nuanced risk profiles so premiums can be adjusted in a dynamic fashion. In fact, IoT has also been instrumental in lowering costs associated with insurance claims processing by up to 30%, per Mordor Intelligence.

However, there's a technical issue, and that involves figuring out how to leverage large datasets. Sensor data can be unstructured - not to mention high-volume.

This is where custom-built software platforms can help. These solutions are capable of ingesting large amounts of data from diverse sources -- both processing and integrating them with legacy systems in real time to save you a ton of time, money and hassle.

With custom software in place, you can tap into the full potential of IoT, thus turning reactive claims into proactive risk management.

AI-Powered Risk Scoring and Underwriting

There's no doubt that IoT has revolutionized risk management -- but so has AI. With AI, you can make sense of the massive, fragmented data streams that keep pouring in from internal systems, connected devices and third-party sources. Plus, fast and smart underwriting is possible with AI.

While underwriting traditionally depended on backward-looking data, AI shifts it to the present by processing real-time data -- contextual, environmental and behavioral -- to generate dynamic risk scores unique to each profile.

As a result, pricing is now not probability-based, relying on historical cohorts. Instead, it reflects real exposure.

Take the recent insights released by McKinsey which show that insurers that use AI in underwriting have witnessed loss ratio improvements of up to 5%. That's not all. They have also seen expense reductions of 10%–15%. And this is just the beginning.

Personalization is another major advantage when it comes to AI insurance models. You can use AI to gather lifestyle factors and wearable data of your customers to craft personalized plans for them with dynamic premiums that adjust to their real-time behavior.

Property insurers can use AI to determine occupancy trends and environmental risks when drafting plans -- a granularity level that was impossible in the days of manual underwriting.

Consequently, with the advent of AI, insurance policies are now not only highly customizable but also very adaptive to changes. Moreover, AI can trigger early interventions, adjust coverages and flag anomalies before the commencement of a renewal cycle.

The presence of AI in insurance might seem futuristic, but incumbents and startups are already leveraging AI-based underwriting engines to prevent fraud and improve accuracy while keeping personalization as the basis for all liaison.

Lemonade uses AI bots and behavioral data to assess risk in real time, settling some claims instantly while reducing loss ratios and operational costs.

Addressing Ethical and Operational Risks in AI Integration

As with anything new, challenges abound with the integration of AI and IoT into the mechanisms of the insurance sector. But none of the threats arise from policyholders. Rather, it's the system itself that poses risks, ranging from algorithmic bias to data privacy and regulatory scrutiny.

You see, IoT devices are responsible for collecting data -- location, behavior, even biometric information -- that can be classified as strictly personal. Use of such information without clear boundaries can be considered a breach of trust and a liability. Thus, for insurers, it is critical to protect the data and have stringent rules for using that data in underwriting, pricing, and claims decisions.

However, that's not the only AI hurdle.

Most AI models can be likened to black boxes -- which means they often make decisions that cannot easily be backed by an explanation. This can put the fairness and accountability of such decisions into question, especially when it comes to sensitive tasks like claims automation, where transparency and equity are a must.

As for regulation, jurisdictions around AI are getting tougher. Auditability and model governance are now standard practices. As an insurer, you must guarantee your system can be monitored, tested and documented for any inherent biases.

The message is clear: Without the ethical use of AI, insurers and agencies can land in hot water.

While having a well-governed AI system can boost compliance, it can also serve as a competitive differentiator -- helping insurers build trust in a world where speed with fairness are paramount.

Building a Future-Ready Risk Management Infrastructure

By the year 2027, the global insurance market is expected to reach $9.8 trillion. That's a CAGR of 12% between 2022 and 2027.

With such rapid growth in store, retrofitted tools or patchwork systems for risk management just won't do. The shift from a reactive strategy to a proactive one requires a solid infrastructure that is equally agile, intelligent and purpose-built.

The first step is to rethink your existing tech stack. Your legacy system can likely process only batches of data instead of the continuous loops that emanate from AI engines or IoT devices.

The result?

Data silos, stalled workflows and zero opportunities for intervention. Either you need to modernize these systems or build custom integrations around them to stay viable. It's the only way.

Going down the custom software solution route will offer you the flexibility to centralize disparate data sources without ripping out your core system. As a consequence, you can automate decision-making and enable modular upgrades that help your company with underwriting, claims, and compliance workflows.

That said, infrastructure isn't simply about redefining your tech stack. It's also about ensuring seamless collaboration.

As a forward-looking insurer, your aim should be to track specialized vendors you can partner with to co-develop tools that suit the operational model of your company. Such strategic alliances can benefit your business by bringing domain expertise, speed and long-term innovation to the table.

To be ready for the future, you must choose wisely. You want your risk strategy to lead in the years ahead, not lag.

Final Thoughts and Strategic Recommendations

Don't treat AI and IoT as just another tech tool that comes and goes. Instead, think of them as the switch that lets you alter your entire risk management strategy from the ground up. Both are key to accurate forecasting, faster response times and loss prevention.

With AI and IoT working together for you, you get an insurance model that benefits carriers and policyholders alike.

However, if you want a competitive advantage, being an early adopter is the only way to go. You need to be willing to address ethical risks that arise as you modernize your infrastructure by investing in the right partnerships.

The path ahead for senior insurance executives is dotted with specific tasks:

• A thorough assessment of where and how your reactive models are falling short
• Prioritization of AI and IoT use cases that offer long-term scalability and near-term wins
• Modernization of legacy systems with custom platforms that enable real-time integration in a seamless fashion
• Formation of strict governance frameworks that foster a culture of fairness, auditability, and compliance

The future of risk management is here.

But the real question for CXOs and underwriting leaders like you is: Are you ready to evolve your risk infrastructure, or are you willing to lose your competitive edge?

The choice is yours.

Change is inevitable; managing it effectively is where the challenge lies. Many transformation initiatives fail, not because of technology itself, but because of how change is managed.

Recently, I had the opportunity to participate in Send's INFUSE webinar on Managing Change, alongside industry experts, where we explored what makes transformation efforts successful and the common pitfalls that organizations face. It was a great discussion, and I wanted to share some of the insights we covered.

The Foundation of Successful Change

One of the biggest problems is poor planning. Too often, organizations become enamored with technology without considering its practical effect at the operational level. A shiny new tool means nothing if it doesn't address real pain points for employees on the ground.

A well-structured change program should include:

• Clear Planning and Defined Success Metrics - Organizations must ask themselves, "What does success look like? What does failure look like?" Without a clear road map, businesses risk implementing solutions that fail to deliver tangible benefits.
• Engaging People Early - The people who use the technology daily should be actively involved in planning and implementation. Their input ensures that the solution is solving real problems.
• Focus on Outcomes, Not Just Processes - Change programs can quickly become overly detailed, leading to loss of sight of the bigger picture. Keeping the end goal in mind helps teams stay aligned and motivated.

Biggest Barrier to Change: The Human Element

While legacy systems and regulatory frameworks are common hurdles in insurance, the biggest barriers are human-centric. Underwriters, IT teams, and change managers often speak different "languages," making it difficult to align on goals. Bridging this gap requires creating a common understanding across all stakeholders.

Another major obstacle is clarity of purpose - many transformation initiatives attempt to solve too many problems at once. Instead of creating a solution that excels in one or two areas, they end up with something that doesn't really hit the mark.

Technology's Role in Change Management

Technology is a critical component of transformation, but it should never be the starting point. The biggest mistake companies make is assuming technology alone will fix broken processes. Instead, organizations should:

• Obsess Over the Business Challenge First - Start with understanding the core problem before selecting a tool.
• View Technology as an Ecosystem - No solution exists in isolation; successful adoption depends on integration with existing processes.
• Avoid the "Silver Bullet" Mindset - No single piece of technology will resolve every issue. Instead, incremental improvements and phased adoption drive the best results.

A key trend emerging is custom-built AI solutions that adapt to individual user needs. In the future, organizations will move away from large, off-the-shelf systems in favor of more tailored, intelligent solutions.

Lessons from Experience

Throughout my career, I've seen many businesses invest heavily in technology, only to struggle with adoption. One of the most effective strategies I've used is implementing a "soul-sucking task list"—asking employees to list their most frustrating daily tasks. If a technology investment doesn't directly address one of these pain points, it's unlikely to gain traction.

In another case, a company assumed it had a standardized process for policy cancellations. However, when we examined it, we found three different workflows being used simultaneously. The lesson? You can't please everyone, but you can focus on outcomes. Once the desired outcome is clear, the process will follow naturally.

Final Thoughts: Embracing a Culture of Change

The key takeaway from our discussion? Diminish fear within your organization. Fear of failure, fear of job loss, fear of the unknown—these are the real barriers to change. By fostering an environment where employees feel safe to adapt and innovate, organizations can bridge the gap between technology and transformation.

As Emma Cullum, head of operational strategy change and excellence at QBE, put it:

"A child born today will experience a year's worth of change in just 11 days by the time they turn 60. The companies that succeed will be the ones that embrace this pace of change, continuously modernizing instead of waiting for a perfect solution."

Change is inevitable, but managing it effectively is where the challenge lies. Technological advancements are moving at an incredible pace, creating numerous opportunities. Many transformation initiatives fail - not because of technology itself, but because of how change is managed.

Recently, I had the opportunity to participate in the INFUSE webinar on Managing Change, alongside industry experts, where we explored what makes transformation efforts successful and the common pitfalls that organizations face. It was a great discussion, and I wanted to share some of the insights we covered.

The Foundation of Successful Change

One of the biggest challenges in implementing change is poor planning. Too often, organizations become enamored with technology without considering its practical effect at the operational level. A shiny new tool means nothing if it doesn't address real pain points for employees on the ground.

A well-structured change program should include:

• Clear Planning & Defined Success Metrics - Organizations must ask themselves, "What does success look like? What does failure look like?" Without a clear roadmap, businesses risk implementing solutions that fail to deliver tangible benefits.

• Engaging People Early - The people who use the technology daily should be actively involved in planning and implementation. Their input ensures that the solution is solving real problems.

• Focus on Outcomes, Not Just Processes - Change programs can quickly become overly detailed, leading to loss of sight of the bigger picture. Keeping the end goal in mind helps teams stay aligned and motivated.

As Matt Carter, practice director at Altus Consulting, put it during the webinar:

"You have to keep an abstracted view of the prize you're going after. Programs evolve quickly, and people lose sight of the bigger picture. Keeping them focused on where they're headed ensures success."

Biggest Barriers to Change: The Human Element

While legacy systems and regulatory frameworks are common hurdles in insurance, the biggest barriers are human-centric. Underwriters, IT teams, and change managers often speak different "languages," making it difficult to align on goals. Bridging this gap requires creating a common understanding across all stakeholders.

Another major obstacle is clarity of purpose - many transformation initiatives attempt to solve too many problems at once. Instead of spreading efforts too thin, organizations should focus on one or two key areas where they can create meaningful effect.

Emma Cullum, head of operational strategy change and excellence at QBE, emphasized this during the discussion:

"One of the biggest challenges I've seen is organizations trying to do too much at once. Instead of creating a solution that excels in one or two areas, they end up with something that doesn't really hit the mark."

Technology's Role in Change Management

Technology is a critical component of transformation, but it should never be the starting point. The biggest mistake companies make is assuming technology alone will fix broken processes. Instead, organizations should:

• Obsess Over the Business Challenge First - Start with understanding the core problem before selecting a tool.

• View Technology as a Connected Ecosystem - No solution exists in isolation, successful adoption depends on integration with existing processes.

• Avoid the 'Silver Bullet' Mindset - No single piece of technology will solve every issue. Instead, incremental improvements and phased adoption drive the best results.

A key trend emerging is custom-built AI solutions that adapt to individual user needs. In the future, organizations will move away from large, off-the-shelf systems in favor of more tailored, intelligent solutions.

Lessons from Experience

Throughout my career, I've seen many businesses invest heavily in technology, only to struggle with adoption. One of the most effective strategies I've used is implementing a "soul-sucking task list"—asking employees to list their most frustrating daily tasks. If a technology investment doesn't directly address one of these pain points, it's unlikely to gain traction.

In another case, a company assumed it had a standardized process for policy cancellations. However, when we examined it, we found three different workflows being used simultaneously. The lesson? You can't please everyone, but you can focus on outcomes. Once the desired outcome is clear, the process will naturally follow.

Final Thoughts: Embracing a Culture of Change

The key takeaway from our discussion? Diminish fear within your organization. Fear of failure, fear of job loss, fear of the unknown—these are the real barriers to change. By fostering an environment where employees feel safe to adapt and innovate, organizations can bridge the gap between technology and transformation.

As Emma Cullum, head of operational strategy change and excellence at QBE, put it:

"A child born today will experience a year's worth of change in just 11 days by the time they turn 60. The companies that succeed will be the ones that embrace this pace of change, continuously modernizing instead of waiting for a perfect solution."

At Send's INFUSE April 2025 webinar, we explored a question many in our industry have been asking for years, but perhaps never more seriously than right now:

Is this the year AI goes mainstream in insurance?

From my vantage point at Sixfold, working directly with underwriting and operations teams to implement AI into core workflows, the answer feels clearer than ever:

Yes, if we focus on execution over experimentation.

The hype around AI is not new. But what's changing in 2025 is that AI is no longer confined to innovation teams or isolated proof-of-concepts. It's showing up in daily underwriting, claims triage, and delegated authority oversight, and doing so in a way that improves business results.

Here are five takeaways from the INFUSE discussion and what I'm seeing in the market right now.

1. We've Moved Past the Chatbot Phase

There was a time when AI in insurance meant a chatbot or a clever email assistant. But that phase is behind us. Today, carriers and MGAs are deploying AI to help triage submissions, extract unstructured data from highly variable documents and emails, and flag risks that fall outside appetite.

In short, AI is no longer theoretical. It's operational.

And insurers are realizing that they've long been ahead of the curve in key areas like data science and predictive modeling. What's new is embedding that intelligence directly into workflows.

2. Practical Uses Are Driving Momentum

One of the reasons AI is sticking this time is because it's solving real problems. Manual document processing. Risk triage. Data normalization. Appetite checks. These are not innovation buzzwords, they're the day-to-day blockers underwriters face, and we now have the tools to tackle them.

AI isn't being dropped into the business from above. It's being built around specific uses that create efficiency and unlock underwriting capacity.

3. AI Should Augment, Not Automate Away

At Sixfold, we're strong believers that AI should support underwriters, not replace them.

During the panel, I mentioned this specifically, and it's something we frequently hear from our users: AI gives underwriters back the time to think, to strategize, and to focus on what matters. It takes on the repetitive, facts-based work, so underwriters can focus on judgment, negotiation, and client relationships.

That distinction is critical. The industry doesn't want AI to take over. It wants AI that empowers its experts and amplifies the impact of every underwriter.

4. You Don't Need to Rip and Replace

One of the most common barriers I hear is, "We want to modernize, but we're still working with legacy systems." The good news is: you don't need a greenfield environment to get started.

At INFUSE, Paul Armstrong from AWS put it perfectly: You can start small. You can integrate modular tools into your underwriting process, things like ingestion, enrichment, or renewal comparison, and test the value before scaling.

The key is to be surgical, not sweeping. Solve a specific problem. Show the result. Then move to the next.

5. Trust Is What Makes It Stick

While we didn't dwell on the term "explainability" during the session, the importance of trust came through loud and clear.

Underwriters want to understand how recommendations are made. They want to know that what the AI is surfacing is based on real logic, not a black box.

If AI is going to become a true partner in underwriting, it has to earn that trust. That means surfacing insights clearly, showing the source of decisions, and giving users the ability to validate what's under the hood.

Final Thought: Execution Wins

So, will 2025 be the year AI finally goes mainstream in insurance?

I believe it can be. But only if we shift from strategy to execution. The technology is ready. The uses are known. What matters now is enabling teams, aligning business owners, and embedding AI where it can drive measurable outcomes.

This isn't about adopting AI for its own sake. It's about solving real problems with tools that work.

And in that sense, AI isn't a futuristic idea anymore, it's just smart business.

Eighty percent of people love personalized solutions, and that includes for insurance.

For decades, insurers have been relying on generalized risk models and broad demographic assumptions to design their policies. But consumers today want more than just general policies. They want plans that suit their unique lifestyle, habits, and needs. Traditional one-size-fits-all policies are no longer relevant to them.

And now, with the entrance of AI, insurers can uncover huge amounts of data and read patterns.

Let's dive deep into this.

What Is Personalization in Insurance?

Personalization in policy means tailoring every aspect of a policy. It starts from coverages and premiums and runs to improving services and communication. Instead of giving the same plan to everyone, you give them something that they need. Completely flexible and relevant policies.

Imagine two people, Alex and Jordan. Alex is a 30-year-old city dweller who cycles to work and has a clean history. Jordan is a 45-year-old suburban resident who drives daily and has a family history of hypertension and prefers telehealth options.

If we personalize a policy for both of them, Alex might get a low premium, while Jordan would get a policy that includes regular health check-ups, diet consultations and more.

Role of AI In Improving Personalization

Let's understand how AI will help the insurance sector.

1. Machine Learning 

Imagine getting a compilation of a policy's data within seconds that lets you study history, buying behavior, and wearable device data. For example, in car insurance, machine learning can access the driving data of someone using telematics and determine whether he is a cautious driver. In health insurance, machine learning can track a user's behavior, including how frequently he is going to the gym, through healthy biometric data. The knowledge can reduce premiums.

2. Predictive Analysis

With predictive analytics and AI, you can assess future risks by reading historical data. This will help mitigate risk overall. If data shows a customer entering a high-risk age group, for instance, he might face certain health problems. So the policy could be amended and preventive health services prescribed. In property insurance, geographic and weather data can be used to predict risk levels and offer personalized coverage.

Four Benefits of AI-Powered Personalization

1. Increasing Customer Satisfaction and Loyalty

When policies and services are completely personalized according to users' needs, they feel valued.

Think about it: A health insurance plan that adjusts to someone's lifestyle goals or a car insurance policy that rewards safe driving builds trust. The more you personalize, the better you can build strong relationships and improve customer satisfaction scores.

2. Reducing Churn Rate Through Relevant Offerings

You can sell generic policies to people, but customers will be disengaged. AI solves the issue. Continuously analyzing customers' data to make better decisions improves offerings. Giving timely recommendations, reminders, or added suggestions feels helpful.

3. Better Risk Management and Profitability

With AI, insurers can assess risk in a very detailed way. Instead of reading the broad-level data, you can now check based on behavior and lifestyle. AI can identify high-risk behavior that informs appropriate pricing and preventive measures.

4. Increased Operational Efficiency and Reduced Errors

With the involvement of AI, insurers can automate tasks like data analysis, policy customization, and enhanced customer interactions. You can start using chatbots and virtual assistants to handle common queries that reduce human intervention. This saves time and money. It also reduces human errors, ensuring faster response to queries.

Challenges and Ethical Considerations

1. Data Privacy and Consent

Providing personalized insurance services requires huge amounts of customer data – starting from wearable device metrics to online behavior. The challenge lies in collecting data and managing it properly. Proper consent must be obtained from customers so their data can be used for product and service improvement. Otherwise, the insurer might face compliance issues with HIPAA or GDPR.

2. Avoiding Bias In Algorithms

The data processed by AI is based on historical information. If there's some societal bias in the past data, this might be reflected in the solutions, as well. AI might unintentionally amplify biased data related to race, gender, or economic disparities.

Conclusion

AI is changing the insurance landscape fast. Insurance planning is becoming more dynamic, with data-driven personalization. Now, insurers can use real-time behavior to predict risks with precision.

The Costly Strategy Hidden in Plain Sight

Every year, property and casualty carriers leave billions on the table—not because of nuclear verdicts, runaway juries, or third-party litigation funding, but because of something far more subtle and entirely under our control: the way we negotiate.

In an era where 99% of litigated claims settle, the cultural instinct on the defense side to “hold back” our strongest arguments has become a billion-dollar blind spot. We ration key negotiating points, fearing we’ll run out of ammo. We save key arguments to “surprise them at trial.” We frame less, anchor less, and persuade less. Meanwhile, the plaintiff bar is doing the opposite—and it’s working.

This isn’t a legal problem. It’s a strategy problem.

And it gets worse.

Not only do we hold back the arguments that matter—we rely on formats that make persuasion nearly impossible. While plaintiff attorneys lead with structured, written advocacy in the form of demand packages, defense teams default to brief, reactive phone calls that suppress advocacy and concede control.

We’re not just saying less—we’re saying it in the least effective way possible.

Defense Negotiation Is the Real Battleground

We are seeing more and more claim organizations taking fewer than 2% of their litigated cases to trial. Many are under 1%. This means 99 out of 100 cases are resolved through negotiation.

Negotiation isn’t a placeholder—it’s the battlefield. The weapons are advocacy, storytelling, anchoring, and framing. And the defense is losing that ground war.

Plaintiff attorneys are presenting persuasive, data-rich demand packages early. They’re setting narratives. They’re leveraging AI tools like EvenUp Law to benchmark value, build urgency, and preempt our defenses. 

There’s a reason EvenUp raised $235 million in 18 months and reached unicorn status. Good persuasion works, and if personal injury attorneys like the results, they’ll come back for more. They are. Because this approach of not concealing their case works. Compelling packages work. Anchoring works—even when the recipient knows they’re being anchored, according to multiple studies!

Meanwhile, defense teams are often confined to hurried phone calls, where the plaintiff attorney dominates the airtime and the adjuster is expected to recall and deploy key defenses from memory. This dynamic favors narrative over nuance, emotion over evidence. It makes storytelling nearly impossible.

We almost never prepare comprehensive offer letters in a manner similar to plaintiff demand packages. When we do, it’s usually just a number, or a number with some boiler plate denials. We don’t sell our offers they way they try to sell their demands. 

In short, we’re losing the negotiation battle by not showing up with our best weapons—or using them at all.

What the Plaintiff Bar Understands That We Ignore

We’ve been programmed not to show our hand. The question is, by whom? Examples of what we hear every day:

#1 - “I’m Saving It for Trial”

This had logic in the 1980s, when trial was common (or in movies, where a surprise reveal causes the jury to gasp). Today, trials are rare, and saving your best arguments for the courtroom is like saving your best sales pitch for a client who’s already walked out. When we don’t use our strongest defenses in the 99% of cases that settle before trial, we’re leaving value on the table. Worse, we’re letting plaintiffs drive up expectations with no rebuttal narrative in place. We’re not framing, we’re not anchoring, we’re not controlling the narrative. Given the legal system environment, we should be using our strong points to avoid trial (not to win at trial in the one out of 100 times a case may find its way there).

#2 - “I Don’t Want to Show My Hand”

This assumes that showing strength is a liability. In negotiation theory, it’s the opposite. Revealing credible, well-supported defenses early can shift expectations, reshape the perceived case value, and create decision-pressure. It’s not about tipping your hand—it’s about owning the story and framing the narrative. Hiding defenses cedes narrative control.

#3 - “I Need Ammo for Later Offers”

This is backwards. If your strongest arguments can drive resolution now, why wait? 

This is like saying, “I don’t want to use my strongest points to persuade the other side to settle now, because I might need them later if they don’t settle.” The plaintiff bar doesn’t hold back information with this goal in mind; we don’t need to, either. Sometimes, the absurdity of holding back becomes clear through analogy. Think about salary negotiation. Imagine asking for $150,000 but saying, “I’ll explain why I deserve it in a few weeks.”  

#4 – “Plaintiff counsel won’t engage early”

This is an argument commonly cited on both sides of the fence. Imagine what plaintiff counsel says after they’ve submitted an extensive demand package, only to get a non-response or simply a counter-number in return. Both sides feel this way. Yet, plaintiff counsel are rational actors. Whether fully engaged or not, persuasion affects their perception of the case. 

#5 – “I’d prefer to negotiate orally rather than in writing”

There is a reason plaintiff attorneys produce written demand packages, rather than just calling the claim professional to run through elements of the demand orally. Put simply, written persuasion in this context is more effective. Precision and documentation matter. Substantive evidence builds credibility. Written persuasion has reference value. And, powerfully, a written offer letter (in most jurisdictions) might just make it to the underlying claimant.

Distracted by Threats We Can’t Control While Overlooking Those we Can

The defense community spends enormous energy discussing external threats: nuclear verdicts, litigation financing, venue shopping, social inflation. These are real—but they’re also out of our control. They don’t require us to change. They allow us to feel victimized.

By contrast, how we choose to advocate is entirely within our control. And right now we’re choosing to hold back key arguments. We’re also choosing to not to write things down, believing it won’t influence the plaintiff attorney or their client—which is exactly how they want us to think.  

Our Call to Action

Today’s plaintiff attorneys are no longer winging it. They’re investing early. They build narratives and leverage data. They’re using AI to strengthen their demand packages, augment them with verdict data and aggregated settlement value intelligence. They target every relevant stakeholder: the adjuster, defense counsel, and even the insured (via hammer letters) And they apply pressure with time-limited demands, designed to trigger urgency and fear of bad faith exposure. 

We must do the same! 

• Develop structured offer packages that counter the persuasive impact of demand packages.
• Don’t hold back key arguments—use them early, when they have a chance to shift the case trajectory.
• Leverage written formats to clarify and reinforce the defense position—don’t rely on bits and pieces raised in phone conversations.
• Stop waiting for mediation to present a persuasive case for settlement—get out in front of it.

Not doing these things is costing our industry billions. We can win this battle. We have the smarts, the tools and the experience. We can be powerful advocates, persuasive negotiators, and we can do better to anchor, frame, and own the narratives of our cases.

Conning recently released its annual State of the States report, which ranks the credit quality of each U.S. state according to 13 key metrics based on balance sheet strength, economic conditions, and socioeconomic trends that determine states' financial standing. This year, important considerations for insurers like climate risk, insurance market stability, and cost of living played a significant role in the analysis.

Conning maintains its "stable" outlook for state credit quality in 2025 even though, five years post-COVID, states are again facing uncertainties, this time in the form of federal-level changes that will require prudent budget management.

This year's "stable" view is consistent with Conning's 2024 State of the States outlook and has been the general trend for the last five years. The view in 2020 was "negative" due to the threat of COVID-19, and in 2023 it was "declining" in anticipation of a weakening economy, higher inflation and tapering federal aid.

In 2025, states face a period of significant transition as federal policies shift toward increased state responsibility, particularly in infrastructure, education and healthcare, where federal funding is expected to decrease from pandemic-era spending levels. Although states generally began fiscal year 2025 with stable budgets and robust rainy-day funds, some are experiencing shortfalls amid increasing costs and declining tax revenues. Some of these shortfalls are self-inflicted: 10 states reduced income tax rates or implemented automatic tax cuts in recent years, hampering their ability to withstand these new challenges. The fiscal outlook is further complicated by emerging challenges ranging from immigration and high-impact weather events to housing affordability (which remains a concern across regions) and insurance market stability.

Nevertheless, Conning's "stable" outlook is based on the belief that states have demonstrated sufficient fiscal resilience to navigate this combination of known challenges and unpredictable developments, although they differ significantly in balance-sheet strength, governance, socioeconomic conditions and the ability to leverage an improved economic environment. Several states have implemented governance mechanisms to trigger special legislative sessions if federal actions substantially affect their budgets. Others have set aside funds to counter federal funding cuts or have created special committees to monitor and respond to federal actions affecting state finances.

Methodology Changes

Developments of the last five years have led to methodology refinements for this year's State of the States analysis, including strategic adjustments to enhance the predictive nature of the state rankings. Specifically, Conning introduced "Catastrophe Losses per Capita" and "Cost of Living Index" metrics to capture economic pressures and disaster impacts that directly affect state finances.

Measuring Catastrophe Losses per Capita recognizes that climate risk has emerged as a critical factor in state credit quality analysis. States face mounting pressure from recurring disasters that will likely have long-term effects on their finances. Climate-related infrastructure damage creates substantial financial demands on state governments, both in repair costs and preventative investments, and adds to potential revenue challenges as tax bases get reduced during and after high-impact weather events. States experiencing frequent catastrophic events, such as Louisiana, which during the past five years averaged $1.48 a year per capita in catastrophe losses (compared with Nevada, where the average was $0.01 per capita), often see population outflows, further eroding the tax base. Climate vulnerability assessments and adaptation planning offer a path forward, and measuring a state's exposure to these risks provides essential insight into its long-term fiscal outlook.

Cost of Living Index, also a new metric, addresses another crucial determinant of state credit quality. Population changes are correlated to the cost of living and significantly affect credit quality because when people leave a state, they effectively transfer their share of public liabilities to the remaining population base, a potentially greater challenge for states with high fixed costs. This can drive remaining residents to seek affordable alternatives elsewhere, perpetuating a cycle of declining population. A shrinking population can substantially reduce state tax revenue unless there are corresponding tax rate increases. Conversely, states with positive population trends can maintain lower tax rates, enhancing their competitiveness.

Together, these metrics offer a holistic view of state economic sustainability, revealing how catastrophes create a feedback loop: driving up insurance costs, raising the overall cost of living, harming population retention, affecting property values, and ultimately determining a state's fiscal capacity to maintain services and manage debt obligations.

Movement Across Rankings

This year's rankings show significant movement across the board, reflecting both the refined methodology and changing economic conditions. Idaho claimed first place with an impressive 11-position climb. Northeastern states like Connecticut (9), New York (21), Delaware (25) and Massachusetts (15) also showed remarkable improvement, jumping 30, 23, 20 and 18 positions, respectively, partly due to their resilience against catastrophic losses.

Among the top five states, Utah (2), North Carolina (3), Nevada (4), and Virginia (5) all showed notable improvements based on their overall economic strength. At the other end of the spectrum, Louisiana fell two places to 50, preceded by Oregon (49), Illinois (48), West Virginia (47), and New Mexico (46), with Kansas experiencing the steepest decline of 28 positions to 44, as a result of various economic challenges.

Electrical fires remain one of the most significant hazards to homes and businesses, causing approximately 51,000 fires annually in the U.S. and resulting in over $1.3 billion in property damage. Despite the frequency and severity of these incidents, many policyholders misunderstand how these fires start and what puts them at risk. These misconceptions can leave homes and lives exposed, making it crucial for insurance professionals to provide accurate information and resources.

Myth #1: "GFCIs and circuit breakers catch all electrical problems."

While circuit breakers and GFCIs are critical for home safety, they are not infallible. Most homes are equipped with GFCIs, AFCIs, and conventional breakers, but very few electrical fires would occur if these devices truly caught every fire hazard. Unfortunately, these devices are primarily designed to detect sudden surges and short circuits, not the slower, hidden electrical faults that often spark fires.

Data from the U.S. Fire Administration shows that electrical fires are increasing in frequency, underscoring the limitations of these safety devices. These fires often originate from micro-arcing, aging connections, and other low-level faults that do not trigger GFCIs, AFCIs, or breakers. Many of these hazards quietly build up behind walls and within appliances, making them even more insidious.

Key Takeaway:

Educate policyholders about the limits of their safety devices. Stress that GFCIs and circuit breakers are only part of a complete safety strategy. They are not a substitute for regular inspections and safe usage.

Myth #2: "New homes don't have electrical fire risks."

Many homeowners believe electrical fires are an issue only in older buildings with aging infrastructure. In reality, newer homes face electrical fire hazards, too—often due to improperly installed wiring, manufacturing defects in modern appliances, or the surge in electrical loads from today's technology.

Ting data reveals that 30% of electrical fire hazards originate in devices and appliances within the home itself, regardless of the building's age. Further, 30% of these hazards are caused by dangerous power conditions from the electric utility company, which are also largely independent of home age. This underscores that even newly built homes aren't immune to these threats.

Key Takeaway:

Policyholders need to understand that no matter how new their home is, proper inspections and safe practices are essential. Encourage them to schedule professional electrical evaluations and avoid overloading circuits.

Myth #3: "I'll see or smell signs of an electrical problem before a fire starts."

It's true that some electrical fire hazards may be accompanied by flickering lights, electrical burning odors, or even audible arcing sounds. Homeowners should always take these warning signs seriously, as they could indicate a dangerous condition that might be caught early.

If homeowners sense these warning signs, they should take immediate action to de-energize the affected circuits and call a qualified electrician to address the problem. However, these signs are often hard to detect, especially when electrical faults are developing behind walls or inside appliances. Because these early clues are so subtle or hidden, many electrical fires occur without any obvious warnings. Homeowners can't rely on these signs alone to keep their families and property safe.

Key Takeaway:

Help policyholders understand that waiting for visible signs is a dangerous gamble. Early detection tools, like smart sensors and professional monitoring, can catch these hidden hazards before they escalate. For instance, the Ting sensor and service detect the vast majority of electrical fire hazards and prevent over 80% of electrical fires that would otherwise occur. This advanced technology provides an additional layer of protection that homeowners can trust, significantly reducing risk and providing peace of mind for both policyholders and insurers.

Final Thoughts

Electrical fires pose a continuing threat, but by debunking these three misconceptions and emphasizing detection tools, insurers can help policyholders reduce risk, prevent costly claims, and create safer homes.

Right before I started on the copy desk of the Wall Street Journal as a young pup, a veteran at the Washington Post wrote a column in which he joked that his job was "to change 'that' to 'which' and 'which' to 'that' every time they appear in copy." I soon learned that that's pretty much how reporters view copy editors, and I've always tried not to be pedantic.  

But little things add up, and as we all try to innovate and drive progress in our crucial industry, I think we'd benefit by being more precise with our language. I griped last summer about how seemingly every company claims to be transforming itself and disrupting the industry. Here, I'll lament a smaller point: that so many issues are treated as white-knucklers or otherwise hyped through modifiers that are somewhere between redundant and meaningless.

Today, for instance, I received an article that said the industry was at a "critical moment of truth." "Moment of truth" wasn't strong enough. We're at a "critical" moment of truth, as though there's some other kind.

Our messages about the importance of what's happening in insurance will be stronger if we come across as less breathless and more careful.

Here is some of the most common offending language to watch for. 

Joseph Heller mocked the word "major" with his character Major Major Major Major in "Catch-22," but it's everywhere in business writing. We don't have a disaster, we have a "major" disaster. We don't have a catastrophe, we have a "major" catastrophe. Every crisis is "major," as is every milestone. Is there even such a thing as a minor disaster, a minor catastrophe, a minor crisis or a minor milestone? 

In Ecclesiastes, the wise King Solomon writes that there is nothing new under the sun. But in business writing, including in insurance, seemingly everything is new under the sun. 

We set "new" records, create "new" products and generate "new" insights, as though it's possible to set an old record or create an old product, or as though anyone would want to generate an old insight. We even brag about "new" innovations, somehow not noting that the word "innovation" literally means new. (The root, novus, is  Latin for new.)

"Proactively" is another one that can almost always go. It works fine if you're talking about being proactive rather than reactive, but it is sprinkled into writing like fairy dust, without any of the magic. 

It gets misused in two ways. One, it's used redundantly. Someone will write about "proactively" warning someone or "proactively" preventing a loss -- but warnings, actions that prevent loss, etc., have to be done ahead of time. You can't reactively warn someone or prevent a loss that's happened. Two, "proactively" gets tossed in as emphasis, A company doesn't just send a communication to policyholders; it "proactively" sends that communication. 

(My theory is that "proactively" came about through a sort of language creep. People want to underscore what they're doing, so they don't just, say, search; they "actively" search. But "actively" wasn't enough for some, so they threw a prefix onto it to tell readers that they're actively, actively taking some action.)

Speaking of meaningless emphasis, why have people started writing about doing things with "intentionality"? All that says is that you did something on purpose -- and I hope you aren't doing things by accident. For good measure, "intentionality" is a bastardized word. The root noun is "intent." Turning that into an adjective gives us "intentional." But there's no reason to add a suffix to "intentional" to come up with a new noun form. "Intent" works just great.

There are plenty of others: the "proven" track record, even though the whole point of a track record is that it can be inspected; all the "successful" launches and ventures, even though no one would write about them if they hadn't succeeded; and so on.

To zoom out, away from what may feel like pedantry, I'm arguing for less redundancy and, in the process, less hype. Not everything is "major" or "new" or "proactive" or whatever. There's plenty of important stuff going on in insurance, and we'll come across as more authoritative if we write about it more sparely and accurately.

Cheers,

Paul